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Joint and Progressive Subspace Analysis (JPSA) With Spatial-Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu
2020 IEEE Transactions on Cybernetics  
To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semisupervised hyperspectral dimensionality reduction (HDR), called  ...  joint and progressive subspace analysis (JPSA).  ...  Cai and Dr. C. Wang for providing MATLAB codes for LPP and manifold alignment algorithms.  ... 
doi:10.1109/tcyb.2020.3028931 pmid:33175688 fatcat:6pupfa7sxzgs5dtnniiwnkwdh4

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction [article]

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu
2020 arXiv   pre-print
To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semi-supervised hyperspectral dimensionality reduction (HDR),  ...  called joint and progressive subspace analysis (JPSA).  ...  Cai and Dr. C. Wang for providing MATLAB codes for LPP and manifold alignment algorithms.  ... 
arXiv:2009.10003v1 fatcat:ua3tpf65cfh4tcirzpoaxtiu2e

Table of contents

2021 IEEE Transactions on Cybernetics  
Wei 3588 Joint and Progressive Subspace Analysis (JPSA) With Spatial-Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . .  ...  Chen 3824 Distributed Heuristic Adaptive Neural Networks With Variance Reduction in Switching Graphs . . . . . . . . . . . . . B. Liu and Z.  ... 
doi:10.1109/tcyb.2021.3088710 fatcat:2bynj6f2rffo7hh2j6a6mkjnti

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [article]

Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
and redundancy of higher dimensional HS signals.  ...  However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity  ...  of spatial-spectral manifold alignment and deep regressive regression.  ... 
arXiv:2103.01449v1 fatcat:jvo4pr5atvfb5kohpslvkhhmky

SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers [article]

Danfeng Hong and Zhu Han and Jing Yao and Lianru Gao and Bing Zhang and Antonio Plaza and Jocelyn Chanussot
2021 arXiv   pre-print
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.  ...  To solve this issue, we rethink HS image classification from a sequential perspective with transformers, and propose a novel backbone network called SpectralFormer.  ...  He, “Non-local neural net- and progressive subspace analysis (jpsa) with spatial-spectral manifold works,” in Proc. CVPR, pp. 7794–7803, 2018.  ... 
arXiv:2107.02988v2 fatcat:iw67o2iwhjafbhhrwogcswyk7u